Self-Correcting Agent
Autonomous ReAct Web Research Agent with Self-Evaluation & Fallback Routing
1. THE PROBLEM
Autonomous LLM search agents often get stuck in repetitive loops or output unverified facts when web tools return noisy or conflicting search data.
2. TECHNICAL APPROACH & DECISIONS
1ReAct Loop with Critique Gate
Implemented a ReAct (Reasoning + Acting) execution loop paired with a secondary Reflection evaluator model that audits search results before synthesizing final answers.
Single-prompt linear LLM search call
Reflection loops detected hallucinated citations and forced the agent to execute refined query searches when initial search results were insufficient.
3. TRADE-OFFS & HONEST REFLECTION
Self-evaluation loops double LLM API calls per research task, but increased output factual verification by over 80%.
4. CONCRETE OUTCOME & METRICS
Delivered a resilient web research agent capable of self-correcting failed tool calls autonomously.